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Record W4403249009 · doi:10.1002/adfm.202414942

Adjustable Humidity Response: Ultrahigh Green EMI Shielding Gel with Double‐Side Constant Temperature Capability

2024· article· en· W4403249009 on OpenAlexaff
Zhengkun Ma, Jingzong He, Shilin Liu, Wenting Zhang, Qilin Wu, Malcolm Xing

Bibliographic record

VenueAdvanced Functional Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsUniversity of Manitoba
FundersFundamental Research Funds for the Central UniversitiesInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsMaterials scienceElectromagnetic shieldingHumidityEMIComposite materialElectromagnetic interferenceThermal conductivityWork (physics)ConductivityMechanical engineeringComputer scienceTelecommunicationsMeteorology

Abstract

fetched live from OpenAlex

Abstract With the increasingly serious secondary pollution of electromagnetic waves (EMWs), efficient green shielding materials have become an urgent need. MXene is widely used in electromagnetic interference (EMI) shielding due to its large surface area and high conductivity. Here, this work constructs MXene islands with better shielding performance through viscosity control and continuous mechanical stirring. Benefiting from the MXene islands, Janus‐structural gel with humidity response is successfully prepared, showing controllable ultrahigh green shielding property (up to 100.5 dB, thickness of 2 mm). Interestingly, this gel can spontaneously adjust its water content according to the humidity, resulting in a reversible transformation in three states to meet the needs of different environments. By Janus‐structural design and reasonable material combination, this gel demonstrates excellent electrothermal (rise to 111.6 °C, 5 s, 2 V)/thermal insulation (thermal conductivity is low to 0.029 W m −1 K −1 ) properties to meet the needs of shielding in underwater/fire operations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.242
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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